Search Results for author: Gunshi Gupta

Found 4 papers, 3 papers with code

Look-ahead Meta Learning for Continual Learning

2 code implementations NeurIPS 2020 Gunshi Gupta, Karmesh Yadav, Liam Paull

The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks.

Continual Learning Meta-Learning

La-MAML: Look-ahead Meta Learning for Continual Learning

3 code implementations ICML Workshop LifelongML 2020 Gunshi Gupta, Karmesh Yadav, Liam Paull

The continual learning problem involves training models with limited capacity to perform well on a set of an unknown number of sequentially arriving tasks.

Continual Learning Meta-Learning

Unifying Variational Inference and PAC-Bayes for Supervised Learning that Scales

1 code implementation23 Oct 2019 Sanjay Thakur, Herke van Hoof, Gunshi Gupta, David Meger

PAC Bayes is a generalized framework which is more resistant to overfitting and that yields performance bounds that hold with arbitrarily high probability even on the unjustified extrapolations.

Variational Inference

Geometric Consistency for Self-Supervised End-to-End Visual Odometry

no code implementations11 Apr 2018 Ganesh Iyer, J. Krishna Murthy, Gunshi Gupta, K. Madhava Krishna, Liam Paull

We show that using a noisy teacher, which could be a standard VO pipeline, and by designing a loss term that enforces geometric consistency of the trajectory, we can train accurate deep models for VO that do not require ground-truth labels.

Visual Odometry

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